----------------------------------------------------------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  /Users/kelebogilezvobgo/Dropbox/1_Research/1_Publications/6_Demanding-Truth/Demanding-Truth/ISQ_FINAL/Data/VTC_Adoption_kz2020demanding
> .log
  log type:  text
 opened on:   5 Jun 2020, 09:23:00

. use "VTC_Adoption_kz2020demanding.dta", clear
(Written by R.              )

. pause on

. 
. 
. 
. ***
. * Descriptive Statistics
. ***
. 
. * Table A1: Summary Statistics of Variables Used in Study One
. eststo clear

. estpost summarize v2xcs_ccsi_avg5  f_media_ai_br_avg5 ingo_avg5 latentmean_avg5 lji_avg5 polconiii_avg5 region_precedent yr /*
> */igo_avg5 gdppc_wdi_avg5 oda_per_avg5 pop_wdi_avg5 duration cumulative_intensity ln_fatality /*
> */democracy_breakdowns

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
v2xcs_ccsi~5 |       668        668    .447974   .0658398   .2565926   .0185258   .9574692   299.2466 
f_media_ai~5 |       527        527   5.980867   27.46168   5.240389          0   35.33333   3151.917 
   ingo_avg5 |       650        650   5.813365   .9792114   .9895511          0   7.943626   3778.687 
latentmean~5 |       674        674  -1.078627   .6536725   .8085002   -2.74733   1.827653  -726.9946 
    lji_avg5 |       665        665   .2676478   .0450227   .2121856      .0162   .9571333   177.9858 
polconiii_~5 |       651        651   .1464734   .0330769   .1818706          0      .6667   95.35421 
region_pre~t |       676        676   2.038462   4.466667   2.113449          0         10       1378 
          yr |       676        676   26.61982   105.5367   10.27311          0         47      17995 
    igo_avg5 |       651        651   3.664618    .321338   .5668668          0   4.332797   2385.666 
gdppc_wdi_~5 |       620        620   7.032301   1.477724   1.215617    5.06597   10.31206   4360.027 
oda_per_avg5 |       587        587   .0590602   .0023781   .0487657   .0189197   .3556653   34.66836 
pop_wdi_avg5 |       674        674   16.76542   2.124274   1.457489   11.24673   20.64018   11299.89 
    duration |       269        269   3.245822   34.98855   5.915112          0   40.36164    873.126 
cumulative~y |       268        268    .488806   .2508106   .5008099          0          1        131 
 ln_fatality |       320        320    4.79917   1.665686   1.290614   3.218876   13.12236   1535.734 
democracy_~s |        86         86   .7906977    .920383    .959366          0          4         68 

. esttab using sumstats_studyone.tex, replace cells("mean(fmt(2)) min max count(fmt(0))") nonum noobs  label
(output written to sumstats_studyone.tex)

.  
. 
. * Table A5: Summary Statistics of New Variables for Varieties of Transition Supplementary Analysis
. eststo clear

. estpost summarize agreement victory_govt victory_rebel distributive_trans

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
   agreement |       269        269   .1152416   .1023415   .3199085          0          1         31 
victory_govt |       269        269   .2007435   .1610442   .4013032          0          1         54 
victory_re~l |       269        269   .0669145   .0626699   .2503396          0          1         18 
distributi~s |        60         60   .5833333   .2471751   .4971671          0          1         35 

. esttab using sumstats_varieties.tex, replace cells("mean(fmt(2)) min max count(fmt(0))") nonum noobs  label
(output written to sumstats_varieties.tex)

.  
.  
.  
. *** 
. * Main analysis
. ***
. 
. * Setting global controls
. 
. global tans v2xcs_ccsi_avg5 f_media_ai_br_avg5 ingo_avg5 

. global dp latentmean_avg5 lji_avg5 polconiii_avg5

. global wp region_precedent yr igo_avg5 

. global econ gdppc_wdi_avg5 oda_per_avg5 pop_wdi_avg5

. global conflict duration cumulative_intensity

. global violence ln_fatality

. global democracy democracy_breakdowns

. 
. * Table 1: Transnational Advocacy and Truth Commission Creation
. eststo clear

. eststo A: logit tc_10 $tans $dp dem conflict, vce(cluster gwno)

Iteration 0:   log pseudolikelihood = -287.61399  
Iteration 1:   log pseudolikelihood = -246.91341  
Iteration 2:   log pseudolikelihood = -244.38326  
Iteration 3:   log pseudolikelihood = -244.37656  
Iteration 4:   log pseudolikelihood = -244.37656  

Logistic regression                             Number of obs     =        523
                                                Wald chi2(8)      =      31.28
                                                Prob > chi2       =     0.0001
Log pseudolikelihood = -244.37656               Pseudo R2         =     0.1503

                                        (Std. Err. adjusted for 98 clusters in gwno)
------------------------------------------------------------------------------------
                   |               Robust
             tc_10 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
   v2xcs_ccsi_avg5 |   3.718214   1.363413     2.73   0.006     1.045974    6.390455
f_media_ai_br_avg5 |   .1434628   .0559571     2.56   0.010     .0337889    .2531367
         ingo_avg5 |   .0049577   .2875066     0.02   0.986    -.5585448    .5684602
   latentmean_avg5 |  -.2371528    .449062    -0.53   0.597    -1.117298    .6429925
          lji_avg5 |  -1.059525   2.281118    -0.46   0.642    -5.530434    3.411384
    polconiii_avg5 |  -3.864267    2.40506    -1.61   0.108    -8.578097    .8495638
               dem |   .5005424   .4915359     1.02   0.309    -.4628503    1.463935
          conflict |  -.9223595    .336549    -2.74   0.006    -1.581983   -.2627356
             _cons |  -3.101529   1.791611    -1.73   0.083    -6.613021    .4099634
------------------------------------------------------------------------------------

. eststo B: logit tc_10 $tans $dp $wp dem conflict, vce(cluster gwno)

Iteration 0:   log pseudolikelihood = -287.61399  
Iteration 1:   log pseudolikelihood = -244.43915  
Iteration 2:   log pseudolikelihood = -241.45533  
Iteration 3:   log pseudolikelihood = -241.44494  
Iteration 4:   log pseudolikelihood = -241.44494  

Logistic regression                             Number of obs     =        523
                                                Wald chi2(11)     =      36.77
                                                Prob > chi2       =     0.0001
Log pseudolikelihood = -241.44494               Pseudo R2         =     0.1605

                                        (Std. Err. adjusted for 98 clusters in gwno)
------------------------------------------------------------------------------------
                   |               Robust
             tc_10 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
   v2xcs_ccsi_avg5 |   4.044528   1.441238     2.81   0.005     1.219753    6.869302
f_media_ai_br_avg5 |   .1486552   .0545741     2.72   0.006      .041692    .2556184
         ingo_avg5 |   .3516543   .5332354     0.66   0.510    -.6934679    1.396776
   latentmean_avg5 |   -.160739    .462959    -0.35   0.728    -1.068122     .746644
          lji_avg5 |  -2.094616   2.602797    -0.80   0.421    -7.196004    3.006773
    polconiii_avg5 |   -4.25406   2.437494    -1.75   0.081    -9.031461    .5233405
  region_precedent |   .1386853   .1446898     0.96   0.338    -.1449016    .4222721
                yr |  -.0369505   .0368637    -1.00   0.316     -.109202    .0353009
          igo_avg5 |  -.6916196   .8422099    -0.82   0.412    -2.342321    .9590814
               dem |   .3578723   .5289139     0.68   0.499    -.6787799    1.394524
          conflict |  -1.005241   .3691177    -2.72   0.006    -1.728698   -.2817835
             _cons |  -1.627405   2.196704    -0.74   0.459    -5.932866    2.678056
------------------------------------------------------------------------------------

. eststo C: logit tc_10 $tans $dp $wp $econ dem conflict, vce(cluster gwno)

Iteration 0:   log pseudolikelihood = -256.64404  
Iteration 1:   log pseudolikelihood = -194.37302  
Iteration 2:   log pseudolikelihood = -187.91274  
Iteration 3:   log pseudolikelihood = -187.79256  
Iteration 4:   log pseudolikelihood = -187.79209  
Iteration 5:   log pseudolikelihood = -187.79209  

Logistic regression                             Number of obs     =        469
                                                Wald chi2(14)     =      36.66
                                                Prob > chi2       =     0.0008
Log pseudolikelihood = -187.79209               Pseudo R2         =     0.2683

                                        (Std. Err. adjusted for 87 clusters in gwno)
------------------------------------------------------------------------------------
                   |               Robust
             tc_10 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
   v2xcs_ccsi_avg5 |   4.343806   1.724952     2.52   0.012     .9629616     7.72465
f_media_ai_br_avg5 |   .2355127   .0717223     3.28   0.001     .0949396    .3760859
         ingo_avg5 |   2.467082   .9266753     2.66   0.008     .6508316    4.283332
   latentmean_avg5 |  -.0802214   .5312916    -0.15   0.880    -1.121534    .9610911
          lji_avg5 |  -6.209749   3.047321    -2.04   0.042    -12.18239     -.23711
    polconiii_avg5 |  -3.093034   2.662892    -1.16   0.245    -8.312205    2.126138
  region_precedent |   .2335116   .1524692     1.53   0.126    -.0653225    .5323458
                yr |  -.0775134   .0425625    -1.82   0.069    -.1609344    .0059076
          igo_avg5 |  -1.865646   1.104705    -1.69   0.091    -4.030829    .2995364
    gdppc_wdi_avg5 |  -.4919022   .4379435    -1.12   0.261    -1.350256    .3664514
      oda_per_avg5 |   2.695538   7.702102     0.35   0.726     -12.4003    17.79138
      pop_wdi_avg5 |  -1.015553   .3466384    -2.93   0.003    -1.694952   -.3361547
               dem |   .1714835   .5599839     0.31   0.759    -.9260649    1.269032
          conflict |  -.7040004   .4246368    -1.66   0.097    -1.536273    .1282724
             _cons |   11.50699   6.813401     1.69   0.091    -1.847028    24.86101
------------------------------------------------------------------------------------

. eststo D: logit tc_10 $tans $dp $wp $econ $conflict, vce(cluster gwno)

Iteration 0:   log pseudolikelihood = -72.205966  
Iteration 1:   log pseudolikelihood = -56.326633  
Iteration 2:   log pseudolikelihood = -52.297381  
Iteration 3:   log pseudolikelihood = -52.160447  
Iteration 4:   log pseudolikelihood = -52.160251  
Iteration 5:   log pseudolikelihood = -52.160251  

Logistic regression                             Number of obs     =        191
                                                Wald chi2(14)     =      69.95
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -52.160251               Pseudo R2         =     0.2776

                                          (Std. Err. adjusted for 64 clusters in gwno)
--------------------------------------------------------------------------------------
                     |               Robust
               tc_10 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
     v2xcs_ccsi_avg5 |   3.274057   1.777091     1.84   0.065    -.2089766    6.757091
  f_media_ai_br_avg5 |    .198298   .0522795     3.79   0.000     .0958321    .3007639
           ingo_avg5 |   .6399068    .613787     1.04   0.297    -.5630935    1.842907
     latentmean_avg5 |  -.2492376   .8524561    -0.29   0.770    -1.920021    1.421546
            lji_avg5 |  -3.966356   3.589188    -1.11   0.269    -11.00103    3.068322
      polconiii_avg5 |   .1661062   3.327356     0.05   0.960    -6.355392    6.687605
    region_precedent |   .4385787   .3202052     1.37   0.171     -.189012    1.066169
                  yr |  -.0135427   .0646362    -0.21   0.834    -.1402273    .1131419
            igo_avg5 |  -.3034974   .8469343    -0.36   0.720    -1.963458    1.356463
      gdppc_wdi_avg5 |  -.0147469   .5094002    -0.03   0.977    -1.013153     .983659
        oda_per_avg5 |   9.306796   9.302683     1.00   0.317    -8.926128    27.53972
        pop_wdi_avg5 |  -.8665404   .5166547    -1.68   0.094    -1.879165    .1460842
            duration |     .04434   .0338521     1.31   0.190    -.0220089    .1106888
cumulative_intensity |   1.005381   .4981781     2.02   0.044       .02897    1.981792
               _cons |   6.108719   10.68547     0.57   0.568    -14.83442    27.05186
--------------------------------------------------------------------------------------

. eststo E: logit tc_10 $tans $dp $wp $econ $violence, vce(cluster gwno)

Iteration 0:   log pseudolikelihood = -134.74585  
Iteration 1:   log pseudolikelihood = -96.052086  
Iteration 2:   log pseudolikelihood = -92.032982  
Iteration 3:   log pseudolikelihood = -91.951955  
Iteration 4:   log pseudolikelihood = -91.951898  
Iteration 5:   log pseudolikelihood = -91.951898  

Logistic regression                             Number of obs     =        221
                                                Wald chi2(13)     =      22.77
                                                Prob > chi2       =     0.0445
Log pseudolikelihood = -91.951898               Pseudo R2         =     0.3176

                                        (Std. Err. adjusted for 43 clusters in gwno)
------------------------------------------------------------------------------------
                   |               Robust
             tc_10 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
   v2xcs_ccsi_avg5 |   3.997851   2.042515     1.96   0.050    -.0054035    8.001106
f_media_ai_br_avg5 |   .2622615   .0862912     3.04   0.002     .0931338    .4313892
         ingo_avg5 |   5.179904   2.293302     2.26   0.024     .6851147    9.674694
   latentmean_avg5 |   .6316297   .5564069     1.14   0.256    -.4589079    1.722167
          lji_avg5 |  -9.164802   4.190497    -2.19   0.029    -17.37803   -.9515792
    polconiii_avg5 |  -4.005695   3.893125    -1.03   0.304    -11.63608    3.624691
  region_precedent |   .2771428   .2272023     1.22   0.223    -.1681654    .7224511
                yr |  -.1991258   .0753455    -2.64   0.008    -.3468003   -.0514514
          igo_avg5 |  -2.611961   1.890842    -1.38   0.167    -6.317943    1.094021
    gdppc_wdi_avg5 |  -1.281052   .7141279    -1.79   0.073    -2.680717    .1186128
      oda_per_avg5 |  -1.387182   10.75134    -0.13   0.897    -22.45942    19.68506
      pop_wdi_avg5 |  -1.395894   .5948982    -2.35   0.019    -2.561873   -.2299147
       ln_fatality |   .1585918   .2098577     0.76   0.450    -.2527216    .5699053
             _cons |   14.05594   8.586444     1.64   0.102    -2.773176    30.88506
------------------------------------------------------------------------------------

. eststo F: logit tc_10 $tans $dp $wp $econ $democracy, vce(cluster gwno)

Iteration 0:   log pseudolikelihood = -37.047541  
Iteration 1:   log pseudolikelihood = -21.170415  
Iteration 2:   log pseudolikelihood =   -19.6456  
Iteration 3:   log pseudolikelihood = -19.477735  
Iteration 4:   log pseudolikelihood = -19.477132  
Iteration 5:   log pseudolikelihood = -19.477132  

Logistic regression                             Number of obs     =         56
                                                Wald chi2(13)     =      24.56
                                                Prob > chi2       =     0.0264
Log pseudolikelihood = -19.477132               Pseudo R2         =     0.4743

                                          (Std. Err. adjusted for 46 clusters in gwno)
--------------------------------------------------------------------------------------
                     |               Robust
               tc_10 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
     v2xcs_ccsi_avg5 |  -.9533006   3.678261    -0.26   0.796    -8.162559    6.255958
  f_media_ai_br_avg5 |   .1062849   .1413134     0.75   0.452    -.1706843     .383254
           ingo_avg5 |    7.14969   3.200837     2.23   0.026      .876166    13.42321
     latentmean_avg5 |  -1.006122   .7684147    -1.31   0.190    -2.512187    .4999434
            lji_avg5 |   -4.33334   4.836735    -0.90   0.370    -13.81317    5.146485
      polconiii_avg5 |  -3.515607   4.267659    -0.82   0.410    -11.88007    4.848851
    region_precedent |   .2184875   .3233289     0.68   0.499    -.4152256    .8522006
                  yr |  -.0571481   .1017496    -0.56   0.574    -.2565736    .1422773
            igo_avg5 |  -1.460946    3.13611    -0.47   0.641    -7.607609    4.685717
      gdppc_wdi_avg5 |  -1.483364   1.039199    -1.43   0.153    -3.520157    .5534287
        oda_per_avg5 |    -5.7351   12.81156    -0.45   0.654    -30.84529    19.37509
        pop_wdi_avg5 |  -2.436357   .8406774    -2.90   0.004    -4.084055   -.7886595
democracy_breakdowns |  -.2056633   .6194688    -0.33   0.740      -1.4198    1.008473
               _cons |   14.72117   12.11573     1.22   0.224    -9.025223    38.46755
--------------------------------------------------------------------------------------

. esttab A B C D E F using study_one.tex, replace unstack label b(2) se(2) /*
> */ nomtitles title("Transnational Advocacy and Truth Commission Adoption") /*
> */ addnotes("All models report clustered standard errors by country.") star(+ 0.10 * 0.05 ** 0.01) 
(output written to study_one.tex)

. 
. 
. 
. *** 
. * Robustness checks and supplementary analyses
. ***
. 
. * Table A4: Transnational Advocacy and Truth Commission Creation (transition = 5 years)
. eststo clear

. eststo A: logit tc_5 $tans $dp dem conflict, vce(cluster gwno)

Iteration 0:   log pseudolikelihood = -222.02862  
Iteration 1:   log pseudolikelihood = -191.42278  
Iteration 2:   log pseudolikelihood = -187.35708  
Iteration 3:   log pseudolikelihood = -187.32367  
Iteration 4:   log pseudolikelihood = -187.32366  

Logistic regression                             Number of obs     =        523
                                                Wald chi2(8)      =      26.78
                                                Prob > chi2       =     0.0008
Log pseudolikelihood = -187.32366               Pseudo R2         =     0.1563

                                        (Std. Err. adjusted for 98 clusters in gwno)
------------------------------------------------------------------------------------
                   |               Robust
              tc_5 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
   v2xcs_ccsi_avg5 |   3.135694   1.496306     2.10   0.036     .2029889      6.0684
f_media_ai_br_avg5 |   .1508815   .0562406     2.68   0.007      .040652    .2611111
         ingo_avg5 |   .2793093   .2153494     1.30   0.195    -.1427678    .7013864
   latentmean_avg5 |  -.0650113   .4489886    -0.14   0.885    -.9450128    .8149903
          lji_avg5 |  -1.751915   2.346562    -0.75   0.455    -6.351092    2.847261
    polconiii_avg5 |  -2.877471   2.631694    -1.09   0.274    -8.035496    2.280554
               dem |   .4942975   .4163738     1.19   0.235    -.3217803    1.310375
          conflict |  -1.060697   .3410179    -3.11   0.002    -1.729079   -.3923136
             _cons |  -4.889017   1.501692    -3.26   0.001     -7.83228   -1.945755
------------------------------------------------------------------------------------

. eststo B: logit tc_5 $tans $dp $wp dem conflict, vce(cluster gwno)

Iteration 0:   log pseudolikelihood = -222.02862  
Iteration 1:   log pseudolikelihood =  -190.9484  
Iteration 2:   log pseudolikelihood = -186.81154  
Iteration 3:   log pseudolikelihood = -186.77416  
Iteration 4:   log pseudolikelihood = -186.77415  

Logistic regression                             Number of obs     =        523
                                                Wald chi2(11)     =      31.00
                                                Prob > chi2       =     0.0011
Log pseudolikelihood = -186.77415               Pseudo R2         =     0.1588

                                        (Std. Err. adjusted for 98 clusters in gwno)
------------------------------------------------------------------------------------
                   |               Robust
              tc_5 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
   v2xcs_ccsi_avg5 |   3.200668   1.523979     2.10   0.036     .2137238    6.187612
f_media_ai_br_avg5 |   .1547197    .051134     3.03   0.002     .0544989    .2549406
         ingo_avg5 |   .3931284   .4863117     0.81   0.419     -.560025    1.346282
   latentmean_avg5 |  -.0258674   .4398077    -0.06   0.953    -.8878747      .83614
          lji_avg5 |  -2.115697   2.736678    -0.77   0.439    -7.479486    3.248093
    polconiii_avg5 |  -3.084281   2.662181    -1.16   0.247     -8.30206    2.133498
  region_precedent |   .0808007   .1413474     0.57   0.568    -.1962351    .3578365
                yr |  -.0139118   .0344152    -0.40   0.686    -.0813645    .0535408
          igo_avg5 |  -.2605322   .9225346    -0.28   0.778    -2.068667    1.547602
               dem |   .4250305   .3971227     1.07   0.284    -.3533158    1.203377
          conflict |  -1.089818   .3486892    -3.13   0.002    -1.773237   -.4064002
             _cons |  -4.256847   2.310016    -1.84   0.065    -8.784396    .2707018
------------------------------------------------------------------------------------

. eststo C: logit tc_5 $tans $dp $wp $econ dem conflict, vce(cluster gwno)

Iteration 0:   log pseudolikelihood = -199.37453  
Iteration 1:   log pseudolikelihood =  -156.8785  
Iteration 2:   log pseudolikelihood = -147.35602  
Iteration 3:   log pseudolikelihood = -146.21959  
Iteration 4:   log pseudolikelihood = -146.20659  
Iteration 5:   log pseudolikelihood = -146.20659  

Logistic regression                             Number of obs     =        469
                                                Wald chi2(14)     =      56.99
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -146.20659               Pseudo R2         =     0.2667

                                        (Std. Err. adjusted for 87 clusters in gwno)
------------------------------------------------------------------------------------
                   |               Robust
              tc_5 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
   v2xcs_ccsi_avg5 |   2.041163   1.663215     1.23   0.220    -1.218677    5.301004
f_media_ai_br_avg5 |   .2235788   .0736092     3.04   0.002     .0793074    .3678502
         ingo_avg5 |   3.397329   1.096268     3.10   0.002     1.248683    5.545975
   latentmean_avg5 |   .1381643   .4908431     0.28   0.778    -.8238706    1.100199
          lji_avg5 |  -4.935537   3.342159    -1.48   0.140    -11.48605    1.614974
    polconiii_avg5 |  -2.605285   3.033437    -0.86   0.390    -8.550713    3.340143
  region_precedent |   .1871815   .1519967     1.23   0.218    -.1107267    .4850896
                yr |  -.0595762   .0459099    -1.30   0.194    -.1495578    .0304055
          igo_avg5 |  -.7971824    1.33656    -0.60   0.551    -3.416792    1.822427
    gdppc_wdi_avg5 |  -.9114492    .441395    -2.06   0.039    -1.776568   -.0463308
      oda_per_avg5 |  -1.969793   8.824896    -0.22   0.823    -19.26627    15.32669
      pop_wdi_avg5 |  -1.249683   .3338136    -3.74   0.000    -1.903945   -.5954203
               dem |   .2836482   .4420929     0.64   0.521     -.582838    1.150134
          conflict |  -.6363948     .34439    -1.85   0.065    -1.311387    .0385971
             _cons |   8.826774   6.022451     1.47   0.143    -2.977014    20.63056
------------------------------------------------------------------------------------

. eststo D: logit tc_5 $tans $dp $wp $econ $conflict, vce(cluster gwno)

Iteration 0:   log pseudolikelihood = -50.058919  
Iteration 1:   log pseudolikelihood = -39.315249  
Iteration 2:   log pseudolikelihood = -31.483165  
Iteration 3:   log pseudolikelihood = -30.510273  
Iteration 4:   log pseudolikelihood = -30.474101  
Iteration 5:   log pseudolikelihood = -30.473914  
Iteration 6:   log pseudolikelihood = -30.473914  

Logistic regression                             Number of obs     =        191
                                                Wald chi2(14)     =      70.47
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -30.473914               Pseudo R2         =     0.3912

                                          (Std. Err. adjusted for 64 clusters in gwno)
--------------------------------------------------------------------------------------
                     |               Robust
                tc_5 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
     v2xcs_ccsi_avg5 |  -.1237982   2.689094    -0.05   0.963    -5.394325    5.146728
  f_media_ai_br_avg5 |   .2399388   .0903954     2.65   0.008     .0627671    .4171105
           ingo_avg5 |   1.254765   1.487479     0.84   0.399     -1.66064     4.17017
     latentmean_avg5 |  -.0154757   .8822268    -0.02   0.986    -1.744608    1.713657
            lji_avg5 |  -2.480729   5.940316    -0.42   0.676    -14.12354    9.162077
      polconiii_avg5 |   2.356231    4.06806     0.58   0.562    -5.617021    10.32948
    region_precedent |   .4115625   .3427339     1.20   0.230    -.2601836    1.083308
                  yr |   .0333225   .0865208     0.39   0.700    -.1362551    .2029001
            igo_avg5 |   1.876927   1.709265     1.10   0.272     -1.47317    5.227024
      gdppc_wdi_avg5 |  -1.182777   .7721496    -1.53   0.126    -2.696162    .3306084
        oda_per_avg5 |  -19.05882   16.01324    -1.19   0.234     -50.4442    12.32657
        pop_wdi_avg5 |   -1.85631   .6221754    -2.98   0.003    -3.075751   -.6368682
            duration |   .0877795   .0322802     2.72   0.007     .0245115    .1510475
cumulative_intensity |   1.635616   1.165038     1.40   0.160    -.6478165    3.919049
               _cons |   18.80888   14.31014     1.31   0.189    -9.238489    46.85625
--------------------------------------------------------------------------------------

. eststo E: logit tc_5 $tans $dp $wp $econ $violence, vce(cluster gwno)

Iteration 0:   log pseudolikelihood = -107.46977  
Iteration 1:   log pseudolikelihood = -84.247483  
Iteration 2:   log pseudolikelihood = -80.947766  
Iteration 3:   log pseudolikelihood = -80.843192  
Iteration 4:   log pseudolikelihood = -80.842782  
Iteration 5:   log pseudolikelihood = -80.842782  

Logistic regression                             Number of obs     =        221
                                                Wald chi2(13)     =      32.75
                                                Prob > chi2       =     0.0019
Log pseudolikelihood = -80.842782               Pseudo R2         =     0.2478

                                        (Std. Err. adjusted for 43 clusters in gwno)
------------------------------------------------------------------------------------
                   |               Robust
              tc_5 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
   v2xcs_ccsi_avg5 |   2.943097   2.219679     1.33   0.185    -1.407393    7.293588
f_media_ai_br_avg5 |   .2503436   .0767494     3.26   0.001     .0999175    .4007697
         ingo_avg5 |   3.239896   1.947398     1.66   0.096    -.5769336    7.056726
   latentmean_avg5 |   .5599496   .5376384     1.04   0.298    -.4938022    1.613701
          lji_avg5 |   -4.38736   4.060575    -1.08   0.280    -12.34594     3.57122
    polconiii_avg5 |  -3.750604   4.482591    -0.84   0.403    -12.53632    5.035113
  region_precedent |   .0044431    .228418     0.02   0.984    -.4432479    .4521341
                yr |  -.0396739   .0590895    -0.67   0.502    -.1554872    .0761394
          igo_avg5 |  -.9851237   1.757168    -0.56   0.575     -4.42911    2.458863
    gdppc_wdi_avg5 |  -.9226801   .6752444    -1.37   0.172    -2.246135    .4007746
      oda_per_avg5 |   2.033961   10.72175     0.19   0.850    -18.98029    23.04821
      pop_wdi_avg5 |  -.9042881   .4924776    -1.84   0.066    -1.869526    .0609502
       ln_fatality |   .1587624   .2069549     0.77   0.443    -.2468618    .5643865
             _cons |    3.43106   8.274068     0.41   0.678    -12.78582    19.64794
------------------------------------------------------------------------------------

. eststo F: logit tc_5 $tans $dp $wp $econ $democracy, vce(cluster gwno)

Iteration 0:   log pseudolikelihood = -32.542487  
Iteration 1:   log pseudolikelihood = -19.570378  
Iteration 2:   log pseudolikelihood = -17.218262  
Iteration 3:   log pseudolikelihood = -16.689921  
Iteration 4:   log pseudolikelihood = -16.663108  
Iteration 5:   log pseudolikelihood = -16.663058  
Iteration 6:   log pseudolikelihood = -16.663058  

Logistic regression                             Number of obs     =         56
                                                Wald chi2(13)     =      37.09
                                                Prob > chi2       =     0.0004
Log pseudolikelihood = -16.663058               Pseudo R2         =     0.4880

                                          (Std. Err. adjusted for 46 clusters in gwno)
--------------------------------------------------------------------------------------
                     |               Robust
                tc_5 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
     v2xcs_ccsi_avg5 |   1.705706     3.0413     0.56   0.575    -4.255132    7.666545
  f_media_ai_br_avg5 |   .2568609   .1724132     1.49   0.136    -.0810628    .5947845
           ingo_avg5 |   8.717382   2.397521     3.64   0.000     4.018327    13.41644
     latentmean_avg5 |  -.1050128   .7591642    -0.14   0.890    -1.592947    1.382922
            lji_avg5 |   -2.01411    5.09031    -0.40   0.692    -11.99093    7.962715
      polconiii_avg5 |  -7.137351   3.910123    -1.83   0.068    -14.80105    .5263485
    region_precedent |   .6015438    .348541     1.73   0.084    -.0815841    1.284672
                  yr |  -.2587335   .1197816    -2.16   0.031     -.493501   -.0239659
            igo_avg5 |   .9330622   3.550489     0.26   0.793    -6.025768    7.891893
      gdppc_wdi_avg5 |  -1.654347   1.042028    -1.59   0.112    -3.696685    .3879912
        oda_per_avg5 |    9.50666   14.35266     0.66   0.508    -18.62403    37.63735
        pop_wdi_avg5 |  -2.382535   .6967723    -3.42   0.001    -3.748184   -1.016887
democracy_breakdowns |  -.1483508   .6347709    -0.23   0.815    -1.392479    1.095777
               _cons |  -3.353673   14.50974    -0.23   0.817    -31.79225     25.0849
--------------------------------------------------------------------------------------

. esttab A B C D E F using study_one_5y.tex, replace unstack label b(2) se(2) /*
> */ nomtitles title("Transnational Advocacy and Truth Commission Adoption (transition = 5 years)") /*
> */ addnotes("All models report clustered standard errors by country.") star(+ 0.10 * 0.05 ** 0.01) 
(output written to study_one_5y.tex)

. 
. 
. * Table A6: Truth Commission Creation, by Conflict Termination Type
. eststo clear

. eststo D: logit tc_10 $tans $dp $wp $econ $conflict, vce(cluster gwno)

Iteration 0:   log pseudolikelihood = -72.205966  
Iteration 1:   log pseudolikelihood = -56.326633  
Iteration 2:   log pseudolikelihood = -52.297381  
Iteration 3:   log pseudolikelihood = -52.160447  
Iteration 4:   log pseudolikelihood = -52.160251  
Iteration 5:   log pseudolikelihood = -52.160251  

Logistic regression                             Number of obs     =        191
                                                Wald chi2(14)     =      69.95
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -52.160251               Pseudo R2         =     0.2776

                                          (Std. Err. adjusted for 64 clusters in gwno)
--------------------------------------------------------------------------------------
                     |               Robust
               tc_10 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
     v2xcs_ccsi_avg5 |   3.274057   1.777091     1.84   0.065    -.2089766    6.757091
  f_media_ai_br_avg5 |    .198298   .0522795     3.79   0.000     .0958321    .3007639
           ingo_avg5 |   .6399068    .613787     1.04   0.297    -.5630935    1.842907
     latentmean_avg5 |  -.2492376   .8524561    -0.29   0.770    -1.920021    1.421546
            lji_avg5 |  -3.966356   3.589188    -1.11   0.269    -11.00103    3.068322
      polconiii_avg5 |   .1661062   3.327356     0.05   0.960    -6.355392    6.687605
    region_precedent |   .4385787   .3202052     1.37   0.171     -.189012    1.066169
                  yr |  -.0135427   .0646362    -0.21   0.834    -.1402273    .1131419
            igo_avg5 |  -.3034974   .8469343    -0.36   0.720    -1.963458    1.356463
      gdppc_wdi_avg5 |  -.0147469   .5094002    -0.03   0.977    -1.013153     .983659
        oda_per_avg5 |   9.306796   9.302683     1.00   0.317    -8.926128    27.53972
        pop_wdi_avg5 |  -.8665404   .5166547    -1.68   0.094    -1.879165    .1460842
            duration |     .04434   .0338521     1.31   0.190    -.0220089    .1106888
cumulative_intensity |   1.005381   .4981781     2.02   0.044       .02897    1.981792
               _cons |   6.108719   10.68547     0.57   0.568    -14.83442    27.05186
--------------------------------------------------------------------------------------

. eststo winner: logit tc_10 $tans $dp $wp $econ $conflict agreement victory_govt victory_rebel, vce(cluster gwno)

Iteration 0:   log pseudolikelihood = -72.205966  
Iteration 1:   log pseudolikelihood = -53.300798  
Iteration 2:   log pseudolikelihood = -46.836309  
Iteration 3:   log pseudolikelihood = -46.147887  
Iteration 4:   log pseudolikelihood = -46.140705  
Iteration 5:   log pseudolikelihood =   -46.1407  
Iteration 6:   log pseudolikelihood =   -46.1407  

Logistic regression                             Number of obs     =        191
                                                Wald chi2(17)     =      62.88
                                                Prob > chi2       =     0.0000
Log pseudolikelihood =   -46.1407               Pseudo R2         =     0.3610

                                          (Std. Err. adjusted for 64 clusters in gwno)
--------------------------------------------------------------------------------------
                     |               Robust
               tc_10 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
     v2xcs_ccsi_avg5 |   2.885361   1.877602     1.54   0.124    -.7946704    6.565393
  f_media_ai_br_avg5 |   .2012341   .0592423     3.40   0.001     .0851213     .317347
           ingo_avg5 |   .6219951   .5584659     1.11   0.265     -.472578    1.716568
     latentmean_avg5 |  -.0555666   .8078757    -0.07   0.945    -1.638974    1.527841
            lji_avg5 |  -2.755782   3.402552    -0.81   0.418    -9.424662    3.913098
      polconiii_avg5 |  -.7701288   3.631751    -0.21   0.832     -7.88823    6.347972
    region_precedent |   .4308231   .3095607     1.39   0.164    -.1759047    1.037551
                  yr |   .0013056   .0583322     0.02   0.982    -.1130234    .1156345
            igo_avg5 |  -.1446622   .8623307    -0.17   0.867    -1.834799    1.545475
      gdppc_wdi_avg5 |  -.0589906   .4359013    -0.14   0.892    -.9133414    .7953602
        oda_per_avg5 |   5.329818   8.575772     0.62   0.534    -11.47839    22.13802
        pop_wdi_avg5 |  -.7450527   .4989019    -1.49   0.135    -1.722882    .2327771
            duration |   .0623231   .0370964     1.68   0.093    -.0103845    .1350306
cumulative_intensity |   1.174928   .6349574     1.85   0.064    -.0695651    2.419422
           agreement |   1.884132   .7180932     2.62   0.009     .4766954    3.291569
        victory_govt |   2.287332   .5937272     3.85   0.000     1.123648    3.451016
       victory_rebel |     1.6294   1.472598     1.11   0.269    -1.256839    4.515639
               _cons |   2.726795   10.19252     0.27   0.789    -17.25018    22.70377
--------------------------------------------------------------------------------------

. esttab D winner using reg_conflict.tex, replace wide  label b(2) se(2) /*
> */ nomtitles title("Truth Commission Adoption, by Conflict Termination Type") /*
> */ addnotes("All models report clustered standard errors by country.") star(+ 0.10 * 0.05 ** 0.01) 
(output written to reg_conflict.tex)

. 
. 
. * Table A7: Truth Commission Creation, by Regime Transition Type
. eststo clear

. eststo F: logit tc_10 $tans $dp $wp $econ $democracy, vce(cluster gwno)

Iteration 0:   log pseudolikelihood = -37.047541  
Iteration 1:   log pseudolikelihood = -21.170415  
Iteration 2:   log pseudolikelihood =   -19.6456  
Iteration 3:   log pseudolikelihood = -19.477735  
Iteration 4:   log pseudolikelihood = -19.477132  
Iteration 5:   log pseudolikelihood = -19.477132  

Logistic regression                             Number of obs     =         56
                                                Wald chi2(13)     =      24.56
                                                Prob > chi2       =     0.0264
Log pseudolikelihood = -19.477132               Pseudo R2         =     0.4743

                                          (Std. Err. adjusted for 46 clusters in gwno)
--------------------------------------------------------------------------------------
                     |               Robust
               tc_10 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
     v2xcs_ccsi_avg5 |  -.9533006   3.678261    -0.26   0.796    -8.162559    6.255958
  f_media_ai_br_avg5 |   .1062849   .1413134     0.75   0.452    -.1706843     .383254
           ingo_avg5 |    7.14969   3.200837     2.23   0.026      .876166    13.42321
     latentmean_avg5 |  -1.006122   .7684147    -1.31   0.190    -2.512187    .4999434
            lji_avg5 |   -4.33334   4.836735    -0.90   0.370    -13.81317    5.146485
      polconiii_avg5 |  -3.515607   4.267659    -0.82   0.410    -11.88007    4.848851
    region_precedent |   .2184875   .3233289     0.68   0.499    -.4152256    .8522006
                  yr |  -.0571481   .1017496    -0.56   0.574    -.2565736    .1422773
            igo_avg5 |  -1.460946    3.13611    -0.47   0.641    -7.607609    4.685717
      gdppc_wdi_avg5 |  -1.483364   1.039199    -1.43   0.153    -3.520157    .5534287
        oda_per_avg5 |    -5.7351   12.81156    -0.45   0.654    -30.84529    19.37509
        pop_wdi_avg5 |  -2.436357   .8406774    -2.90   0.004    -4.084055   -.7886595
democracy_breakdowns |  -.2056633   .6194688    -0.33   0.740      -1.4198    1.008473
               _cons |   14.72117   12.11573     1.22   0.224    -9.025223    38.46755
--------------------------------------------------------------------------------------

. eststo hkt_dist: logit tc_10 $tans $dp $wp $econ $democracy distributive_trans, vce(cluster gwno)

Iteration 0:   log pseudolikelihood = -27.818536  
Iteration 1:   log pseudolikelihood = -16.013064  
Iteration 2:   log pseudolikelihood =  -15.19716  
Iteration 3:   log pseudolikelihood = -15.107427  
Iteration 4:   log pseudolikelihood = -15.107115  
Iteration 5:   log pseudolikelihood = -15.107115  

Logistic regression                             Number of obs     =         41
                                                Wald chi2(14)     =      29.46
                                                Prob > chi2       =     0.0091
Log pseudolikelihood = -15.107115               Pseudo R2         =     0.4569

                                          (Std. Err. adjusted for 38 clusters in gwno)
--------------------------------------------------------------------------------------
                     |               Robust
               tc_10 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
     v2xcs_ccsi_avg5 |  -.7494223   4.257777    -0.18   0.860    -9.094511    7.595667
  f_media_ai_br_avg5 |   .1602011   .1260919     1.27   0.204    -.0869344    .4073366
           ingo_avg5 |    5.65508   4.133398     1.37   0.171    -2.446231    13.75639
     latentmean_avg5 |  -1.480907   .8808003    -1.68   0.093    -3.207244    .2454293
            lji_avg5 |  -5.074318   5.027146    -1.01   0.313    -14.92734    4.778707
      polconiii_avg5 |  -.7934883   5.706293    -0.14   0.889    -11.97762    10.39064
    region_precedent |   .3913489   .3576496     1.09   0.274    -.3096314    1.092329
                  yr |  -.0607462   .1248939    -0.49   0.627    -.3055338    .1840415
            igo_avg5 |  -1.399464   2.597894    -0.54   0.590    -6.491242    3.692314
      gdppc_wdi_avg5 |  -.6134777   1.237076    -0.50   0.620    -3.038102    1.811147
        oda_per_avg5 |   9.627965   21.81311     0.44   0.659    -33.12494    52.38087
        pop_wdi_avg5 |  -2.062023   1.197252    -1.72   0.085    -4.408594    .2845475
democracy_breakdowns |  -.3739707   .8254558    -0.45   0.651    -1.991834    1.243893
  distributive_trans |   .8430741   1.191121     0.71   0.479    -1.491481    3.177629
               _cons |   8.310945   10.90656     0.76   0.446    -13.06551     29.6874
--------------------------------------------------------------------------------------

. esttab F hkt_dist using reg_democracy.tex, replace wide label b(2) se(2) /*
> */ nomtitles title("Truth Commission Adoption, by Regime Transition Type") /*
> */ addnotes("All models report clustered standard errors by country.") star(+ 0.10 * 0.05 ** 0.01) 
(output written to reg_democracy.tex)

. 
. 
. * Table A8:  Single Observation Survival Analysis for Truth Commission Creation >> See R files
. 
. 
. * Table A9: Truth Commission Creation, with Interaction Effects
. eststo C: logit tc_10 $tans $dp $wp $econ dem conflict, vce(cluster gwno)

Iteration 0:   log pseudolikelihood = -256.64404  
Iteration 1:   log pseudolikelihood = -194.37302  
Iteration 2:   log pseudolikelihood = -187.91274  
Iteration 3:   log pseudolikelihood = -187.79256  
Iteration 4:   log pseudolikelihood = -187.79209  
Iteration 5:   log pseudolikelihood = -187.79209  

Logistic regression                             Number of obs     =        469
                                                Wald chi2(14)     =      36.66
                                                Prob > chi2       =     0.0008
Log pseudolikelihood = -187.79209               Pseudo R2         =     0.2683

                                        (Std. Err. adjusted for 87 clusters in gwno)
------------------------------------------------------------------------------------
                   |               Robust
             tc_10 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
   v2xcs_ccsi_avg5 |   4.343806   1.724952     2.52   0.012     .9629616     7.72465
f_media_ai_br_avg5 |   .2355127   .0717223     3.28   0.001     .0949396    .3760859
         ingo_avg5 |   2.467082   .9266753     2.66   0.008     .6508316    4.283332
   latentmean_avg5 |  -.0802214   .5312916    -0.15   0.880    -1.121534    .9610911
          lji_avg5 |  -6.209749   3.047321    -2.04   0.042    -12.18239     -.23711
    polconiii_avg5 |  -3.093034   2.662892    -1.16   0.245    -8.312205    2.126138
  region_precedent |   .2335116   .1524692     1.53   0.126    -.0653225    .5323458
                yr |  -.0775134   .0425625    -1.82   0.069    -.1609344    .0059076
          igo_avg5 |  -1.865646   1.104705    -1.69   0.091    -4.030829    .2995364
    gdppc_wdi_avg5 |  -.4919022   .4379435    -1.12   0.261    -1.350256    .3664514
      oda_per_avg5 |   2.695538   7.702102     0.35   0.726     -12.4003    17.79138
      pop_wdi_avg5 |  -1.015553   .3466384    -2.93   0.003    -1.694952   -.3361547
               dem |   .1714835   .5599839     0.31   0.759    -.9260649    1.269032
          conflict |  -.7040004   .4246368    -1.66   0.097    -1.536273    .1282724
             _cons |   11.50699   6.813401     1.69   0.091    -1.847028    24.86101
------------------------------------------------------------------------------------

. eststo interaction: logit tc_10 $tans $dp $wp $econ dem conflict domestic_shaming, vce(cluster gwno)

Iteration 0:   log pseudolikelihood = -256.64404  
Iteration 1:   log pseudolikelihood = -194.12924  
Iteration 2:   log pseudolikelihood =  -187.7305  
Iteration 3:   log pseudolikelihood = -187.60851  
Iteration 4:   log pseudolikelihood = -187.60808  
Iteration 5:   log pseudolikelihood = -187.60807  

Logistic regression                             Number of obs     =        469
                                                Wald chi2(15)     =      36.94
                                                Prob > chi2       =     0.0013
Log pseudolikelihood = -187.60807               Pseudo R2         =     0.2690

                                        (Std. Err. adjusted for 87 clusters in gwno)
------------------------------------------------------------------------------------
                   |               Robust
             tc_10 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
   v2xcs_ccsi_avg5 |   3.901443   2.029118     1.92   0.055    -.0755564    7.878442
f_media_ai_br_avg5 |   .2033885   .1151908     1.77   0.077    -.0223814    .4291584
         ingo_avg5 |   2.463927   .9026467     2.73   0.006     .6947718    4.233082
   latentmean_avg5 |  -.0608698   .5228895    -0.12   0.907    -1.085714    .9639748
          lji_avg5 |  -6.274556   3.038601    -2.06   0.039    -12.23011   -.3190071
    polconiii_avg5 |  -3.213622   2.741316    -1.17   0.241    -8.586504    2.159259
  region_precedent |   .2387681   .1530247     1.56   0.119    -.0611548     .538691
                yr |   -.075573   .0405812    -1.86   0.063    -.1551107    .0039647
          igo_avg5 |  -1.871894   1.098821    -1.70   0.088    -4.025543    .2817547
    gdppc_wdi_avg5 |  -.4809644   .4280932    -1.12   0.261    -1.320012    .3580828
      oda_per_avg5 |   2.532269      7.689     0.33   0.742    -12.53789    17.60243
      pop_wdi_avg5 |  -1.018167   .3494988    -2.91   0.004    -1.703172    -.333162
               dem |    .190575   .5498706     0.35   0.729    -.8871516    1.268302
          conflict |  -.7077144   .4247838    -1.67   0.096    -1.540275    .1248465
  domestic_shaming |   .0718963   .1884639     0.38   0.703    -.2974862    .4412787
             _cons |   11.73244   7.044238     1.67   0.096    -2.074009     25.5389
------------------------------------------------------------------------------------

. esttab C interaction using interaction.tex, replace wide  label b(2) se(2) /*
> */ nomtitles title("Truth Commission Adoption, by Conflict Termination Type") /*
> */ addnotes("All models report clustered standard errors by country.") star(+ 0.10 * 0.05 ** 0.01) 
(output written to interaction.tex)

. 
. 
. 
. ***
. * Correlation Test: Independent Variables
. ***
. corr v2xcs_ccsi_avg5 ingo_avg5
(obs=647)

             | v2xcs_~5 ingo_a~5
-------------+------------------
v2xcs_ccsi~5 |   1.0000
   ingo_avg5 |   0.3666   1.0000


. corr ingo_avg5 f_media_ai_br_avg5
(obs=523)

             | ingo_a~5 f_medi~5
-------------+------------------
   ingo_avg5 |   1.0000
f_media_ai~5 |   0.2542   1.0000


. corr v2xcs_ccsi_avg5 f_media_ai_br_avg5
(obs=526)

             | v2xcs_~5 f_medi~5
-------------+------------------
v2xcs_ccsi~5 |   1.0000
f_media_ai~5 |  -0.0401   1.0000


. 
. 
. 
. ***
. * Table A2:  Nearest Neighbor Matching
. ***
. 
. eststo NNM1: teffects nnmatch (tc_10 $tans $dp $wp $econ) (dem), biasadj($tans $dp $wp $econ) vce(iid)

Treatment-effects estimation                   Number of obs      =        469
Estimator      : nearest-neighbor matching     Matches: requested =          1
Outcome model  : matching                                     min =          1
Distance metric: Mahalanobis                                  max =          2
------------------------------------------------------------------------------
       tc_10 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
ATE          |
         dem |
   (1 vs 0)  |   .0399677   .0713853     0.56   0.576    -.0999448    .1798802
------------------------------------------------------------------------------

. eststo NNM2:  teffects nnmatch (tc_10 $tans $dp $wp $econ) (conflict), biasadj($tans $dp $wp $econ) vce(iid)

Treatment-effects estimation                   Number of obs      =        469
Estimator      : nearest-neighbor matching     Matches: requested =          1
Outcome model  : matching                                     min =          1
Distance metric: Mahalanobis                                  max =          4
------------------------------------------------------------------------------
       tc_10 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
ATE          |
    conflict |
   (1 vs 0)  |  -.0229182   .0329143    -0.70   0.486    -.0874291    .0415927
------------------------------------------------------------------------------

. 
. esttab NNM1 NNM2 using nnm_.tex, replace  label b(2) se(2) /*
> */ nomtitles title("Nearest-Neighbor Matching") /*
> */ addnotes("All models report clustered standard errors by country.") star(+ 0.10 * 0.05 ** 0.01)  
(output written to nnm_.tex)

. 
. 
. 
. *** 
. * Predicted Effects, Substantive Effects, Figures
. ***
. 
. *remove LaTeX formatting for labels for logged variables
. label variable ingo_avg5 "Network Access (ln)"

. label variable igo_avg5 "IGO Membership (ln)"

. label variable gdppc_wdi_avg5 "GDP per capita (ln)"

. label variable oda_per_avg5 "ODA as % of GDP"

. label variable pop_wdi_avg5 "Population (ln)"

. label variable ln_fatality "Civilian Killings (ln)"

. 
. 
. * Coefficient Plot 
. 
. * Figure 3: Predicted Effect of Variables on the Likelihood of Truth Commissions
. sem (tc_10 <- $tans $dp $wp $econ dem conflict, vce(cluster gwno))
(207 observations with missing values excluded)

Endogenous variables

Observed:  tc_10

Exogenous variables

Observed:  v2xcs_ccsi_avg5 f_media_ai_br_avg5 ingo_avg5 latentmean_avg5 lji_avg5 polconiii_avg5 region_precedent yr igo_avg5 gdppc_wdi_avg5
           oda_per_avg5 pop_wdi_avg5 dem conflict

Fitting target model:

Iteration 0:   log pseudolikelihood = -4973.5654  
Iteration 1:   log pseudolikelihood = -4973.5654  

Structural equation model                       Number of obs     =        469
Estimation method  = ml
Log pseudolikelihood= -4973.5654

                                          (Std. Err. adjusted for 87 clusters in gwno)
--------------------------------------------------------------------------------------
                     |               Robust
                     |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
Structural           |
  tc_10 <-           |
     v2xcs_ccsi_avg5 |   .5971869   .1902219     3.14   0.002     .2243588     .970015
  f_media_ai_br_avg5 |   .0338639   .0097219     3.48   0.000     .0148094    .0529184
           ingo_avg5 |   .2748641    .076446     3.60   0.000     .1250327    .4246955
     latentmean_avg5 |  -.0200999   .0738054    -0.27   0.785    -.1647559    .1245561
            lji_avg5 |  -.7439395   .3485268    -2.13   0.033    -1.427039   -.0608394
      polconiii_avg5 |  -.4477911   .3312349    -1.35   0.176       -1.097    .2014174
    region_precedent |   .0317775   .0243574     1.30   0.192    -.0159622    .0795171
                  yr |  -.0093779    .005003    -1.87   0.061    -.0191836    .0004279
            igo_avg5 |   -.211954   .1100446    -1.93   0.054    -.4276374    .0037295
      gdppc_wdi_avg5 |  -.0505354   .0520679    -0.97   0.332    -.1525866    .0515157
        oda_per_avg5 |   .6369207   1.298733     0.49   0.624    -1.908549    3.182391
        pop_wdi_avg5 |  -.1307747   .0462568    -2.83   0.005    -.2214364    -.040113
                 dem |   .0465873   .0892084     0.52   0.602    -.1282581    .2214326
            conflict |  -.0735004   .0589774    -1.25   0.213     -.189094    .0420932
               _cons |   1.891352   1.019342     1.86   0.064     -.106522    3.889227
---------------------+----------------------------------------------------------------
         var(e.tc_10)|   .1343028    .020189                      .1000296     .180319
--------------------------------------------------------------------------------------

. coefplot, drop(_cons) xline(0) b(b_std) v(V_std) xtitle(Standardized Coefficients) level(95)

. graph export "/Users/kelebogilezvobgo/Dropbox/1_Research/1_Publications/6_Demanding-Truth/Demanding-Truth/ISQ_FINAL/Data/standardized_main_coefplo
> t.eps", replace
(file /Users/kelebogilezvobgo/Dropbox/1_Research/1_Publications/6_Demanding-Truth/Demanding-Truth/ISQ_FINAL/Data/standardized_main_coefplot.eps writ
> ten in EPS format)

. 
. 
. * Marginal Effects and Plots 
. logit tc_10 $tans $dp $wp $econ dem conflict, vce(cluster gwno)

Iteration 0:   log pseudolikelihood = -256.64404  
Iteration 1:   log pseudolikelihood = -194.37302  
Iteration 2:   log pseudolikelihood = -187.91274  
Iteration 3:   log pseudolikelihood = -187.79256  
Iteration 4:   log pseudolikelihood = -187.79209  
Iteration 5:   log pseudolikelihood = -187.79209  

Logistic regression                             Number of obs     =        469
                                                Wald chi2(14)     =      36.66
                                                Prob > chi2       =     0.0008
Log pseudolikelihood = -187.79209               Pseudo R2         =     0.2683

                                        (Std. Err. adjusted for 87 clusters in gwno)
------------------------------------------------------------------------------------
                   |               Robust
             tc_10 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
   v2xcs_ccsi_avg5 |   4.343806   1.724952     2.52   0.012     .9629616     7.72465
f_media_ai_br_avg5 |   .2355127   .0717223     3.28   0.001     .0949396    .3760859
         ingo_avg5 |   2.467082   .9266753     2.66   0.008     .6508316    4.283332
   latentmean_avg5 |  -.0802214   .5312916    -0.15   0.880    -1.121534    .9610911
          lji_avg5 |  -6.209749   3.047321    -2.04   0.042    -12.18239     -.23711
    polconiii_avg5 |  -3.093034   2.662892    -1.16   0.245    -8.312205    2.126138
  region_precedent |   .2335116   .1524692     1.53   0.126    -.0653225    .5323458
                yr |  -.0775134   .0425625    -1.82   0.069    -.1609344    .0059076
          igo_avg5 |  -1.865646   1.104705    -1.69   0.091    -4.030829    .2995364
    gdppc_wdi_avg5 |  -.4919022   .4379435    -1.12   0.261    -1.350256    .3664514
      oda_per_avg5 |   2.695538   7.702102     0.35   0.726     -12.4003    17.79138
      pop_wdi_avg5 |  -1.015553   .3466384    -2.93   0.003    -1.694952   -.3361547
               dem |   .1714835   .5599839     0.31   0.759    -.9260649    1.269032
          conflict |  -.7040004   .4246368    -1.66   0.097    -1.536273    .1282724
             _cons |   11.50699   6.813401     1.69   0.091    -1.847028    24.86101
------------------------------------------------------------------------------------

. 
. * Figure 4: Effect of Strong Domestic Civil Society, with 95% CIs
. margins, at(v2xcs_ccsi_avg5=(0(.1)1))

Predictive margins                              Number of obs     =        469
Model VCE    : Robust

Expression   : Pr(tc_10), predict()

1._at        : v2xcs_ccsi~5    =           0

2._at        : v2xcs_ccsi~5    =          .1

3._at        : v2xcs_ccsi~5    =          .2

4._at        : v2xcs_ccsi~5    =          .3

5._at        : v2xcs_ccsi~5    =          .4

6._at        : v2xcs_ccsi~5    =          .5

7._at        : v2xcs_ccsi~5    =          .6

8._at        : v2xcs_ccsi~5    =          .7

9._at        : v2xcs_ccsi~5    =          .8

10._at       : v2xcs_ccsi~5    =          .9

11._at       : v2xcs_ccsi~5    =           1

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .0706796   .0404093     1.75   0.080    -.0085212    .1498804
          2  |   .0977232   .0419051     2.33   0.020     .0155908    .1798557
          3  |   .1321481   .0405912     3.26   0.001     .0525908    .2117053
          4  |   .1744359   .0375737     4.64   0.000     .1007928     .248079
          5  |   .2245166   .0368601     6.09   0.000     .1522721     .296761
          6  |    .281681   .0438067     6.43   0.000     .1958214    .3675406
          7  |   .3446042   .0586518     5.88   0.000     .2296488    .4595596
          8  |    .411494   .0773405     5.32   0.000     .2599095    .5630786
          9  |   .4803103   .0961788     4.99   0.000     .2918033    .6688173
         10  |    .548976   .1127096     4.87   0.000     .3280692    .7698828
         11  |   .6155435   .1254611     4.91   0.000     .3696443    .8614427
------------------------------------------------------------------------------

. marginsplot, graphregion(fcolor(white)) recast(scatter) ytitle("Pr(TruthCommission)", size(small)) title("", size(small))

  Variables that uniquely identify margins: v2xcs_ccsi_avg5

. graph export "/Users/kelebogilezvobgo/Dropbox/1_Research/1_Publications/6_Demanding-Truth/Demanding-Truth/ISQ_FINAL/Data/margins_robust_cs.eps", r
> eplace
(file /Users/kelebogilezvobgo/Dropbox/1_Research/1_Publications/6_Demanding-Truth/Demanding-Truth/ISQ_FINAL/Data/margins_robust_cs.eps written in EP
> S format)

. 
. * Figure 5: Effect of INGO Naming and Shaming, with 95% CIs 
. margins, at(f_media_ai_br_avg5=(0(5)36))

Predictive margins                              Number of obs     =        469
Model VCE    : Robust

Expression   : Pr(tc_10), predict()

1._at        : f_media_ai~5    =           0

2._at        : f_media_ai~5    =           5

3._at        : f_media_ai~5    =          10

4._at        : f_media_ai~5    =          15

5._at        : f_media_ai~5    =          20

6._at        : f_media_ai~5    =          25

7._at        : f_media_ai~5    =          30

8._at        : f_media_ai~5    =          35

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .1010789    .029394     3.44   0.001     .0434677    .1586901
          2  |   .2139485   .0305873     6.99   0.000     .1539986    .2738985
          3  |    .385042   .0656971     5.86   0.000      .256278     .513806
          4  |   .5854438     .11215     5.22   0.000     .3656339    .8052537
          5  |   .7642208   .1247007     6.13   0.000     .5198119     1.00863
          6  |   .8851241   .0983992     9.00   0.000     .6922651    1.077983
          7  |   .9503133   .0611424    15.54   0.000     .8304764     1.07015
          8  |   .9799794   .0321903    30.44   0.000     .9168875    1.043071
------------------------------------------------------------------------------

. marginsplot, graphregion(fcolor(white)) recast(scatter) ytitle("Pr(TruthCommission)", size(small)) title("", size(medium small))

  Variables that uniquely identify margins: f_media_ai_br_avg5
(note:  named style medium small not found in class gsize, default attributes used)

. graph export "/Users/kelebogilezvobgo/Dropbox/1_Research/1_Publications/6_Demanding-Truth/Demanding-Truth/ISQ_FINAL/Data/margins_ai_br.eps", repla
> ce
(file /Users/kelebogilezvobgo/Dropbox/1_Research/1_Publications/6_Demanding-Truth/Demanding-Truth/ISQ_FINAL/Data/margins_ai_br.eps written in EPS fo
> rmat)

. 
. * Figure 6: Effect of Network Access, with 95% CIs
. margins, at(ingo_avg5=(0(1)8))

Predictive margins                              Number of obs     =        469
Model VCE    : Robust

Expression   : Pr(tc_10), predict()

1._at        : ingo_avg5       =           0

2._at        : ingo_avg5       =           1

3._at        : ingo_avg5       =           2

4._at        : ingo_avg5       =           3

5._at        : ingo_avg5       =           4

6._at        : ingo_avg5       =           5

7._at        : ingo_avg5       =           6

8._at        : ingo_avg5       =           7

9._at        : ingo_avg5       =           8

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   3.11e-06   .0000151     0.21   0.837    -.0000264    .0000327
          2  |   .0000365   .0001473     0.25   0.804    -.0002521    .0003252
          3  |   .0004154   .0013123     0.32   0.752    -.0021566    .0029874
          4  |   .0036855    .007066     0.52   0.602    -.0101635    .0175345
          5  |   .0191754   .0154328     1.24   0.214    -.0110722    .0494231
          6  |   .0881432   .0279951     3.15   0.002     .0332738    .1430126
          7  |   .3136301   .0556923     5.63   0.000     .2044751    .4227851
          8  |   .6636286   .1195961     5.55   0.000     .4292245    .8980326
          9  |   .8927017    .079175    11.28   0.000     .7375215    1.047882
------------------------------------------------------------------------------

. marginsplot, graphregion(fcolor(white)) recast(scatter)  ytitle("Pr(TruthCommission)", size(small)) title("", size(medium small))

  Variables that uniquely identify margins: ingo_avg5
(note:  named style medium small not found in class gsize, default attributes used)

. graph export "/Users/kelebogilezvobgo/Dropbox/1_Research/1_Publications/6_Demanding-Truth/Demanding-Truth/ISQ_FINAL/Data/margins_network.eps", rep
> lace
(file /Users/kelebogilezvobgo/Dropbox/1_Research/1_Publications/6_Demanding-Truth/Demanding-Truth/ISQ_FINAL/Data/margins_network.eps written in EPS 
> format)

. 
. 
. 
. 
. 
. /****************
>         STUDY TWO       
> *****************/      
. 
. clear all

. set more off

. capture log close
